Data Acceleration

Configure local acceleration for datasets in Spice for faster queries (test)

Datasets can be locally accelerated by the Spice runtime, pulling data from any Data Connector and storing it locally in a Data Accelerator for faster access. The data can be kept up-to-date in real-time or on a refresh schedule, ensuring users always have the latest data locally for querying.

Supported Data Accelerators

Dataset acceleration is enabled by setting the acceleration configuration. Spice currently supports In-Memory Arrow, DuckDB, SQLite, PostgreSQL as accelerators. For engine specific configuration, see Data Accelerator Documentation

Example - Locally Accelerating taxi_trips with Arrow Accelerator

datasets:
  - from: spice.ai/spiceai/quickstart/datasets/taxi_trips
    name: taxi_trips
    acceleration:
      enabled: true
      refresh_mode: full
      refresh_check_interval: 10s

Refresh Modes

Spice supports three modes to refresh/update locally accelerated data from a connected data source. full is the default mode. Refer to Data Refresh documentation for detailed refresh usage and configuration.

Mode	Description	Example
full	Replace/overwrite the entire dataset on each refresh	A table of users
append	Append/add data to the dataset on each refresh	Append-only, immutable datasets, such as time-series or log data
changes	Apply incremental changes	Customer order lifecycle table

Example - Accelerate with arrow accelerator under full refresh mode

datasets:
  - from: databricks:my_dataset
    name: accelerated_dataset
    acceleration:
      refresh_mode: full
      refresh_check_interval: 10m

Indexes

Database indexes are essential for optimizing query performance. Configure indexes for accelerators via indexes field. For detailed configuration, refer to the index documentation.

Example - Configure indexes with SQLite Accelerator

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      indexes:
        number: enabled # Index the `number` column
        '(hash, timestamp)': unique # Add a unique index with a multicolumn key comprised of the `hash` and `timestamp` columns

Constraints

Constraints enforce data integrity in a database. Spice supports constraints on locally accelerated tables to ensure data quality and configure behavior for data updates that violate constraints.

Constraints are specified using column references in the Spicepod via the primary_key field in the acceleration configuration. Additional unique constraints are specified via the indexes field with the value unique. Data that violates these constraints will result in a conflict. For constraints configuration details, visit Constraints Documentation.

Example - Configure primary key constraints with SQLite Accelerator

datasets:
  - from: spice.ai/eth.recent_blocks
    name: eth.recent_blocks
    acceleration:
      enabled: true
      engine: sqlite
      primary_key: hash # Define a primary key on the `hash` column
      indexes:
        '(number, timestamp)': unique # Add a unique index with a multicolumn key comprised of the `number` and `timestamp` columns